RF spatial multiplexing using MIMO-based methods has typically focused on environmental sources of multi-path as a means to achieve spatial multiplexing gain. Thus, MIMO spatial multiplexing schemes either rely on environmentally derived multipath at lower carrier frequencies, or do not use scalable and adaptable MIMO algorithms with predictive complexity when operating in line of sight (LOS) channels. Additionally, these methods are not adapted based on spatial correlation. Meanwhile, analog schemes to achieve spatial multiplexing suffer from an inability to adapt easily to channel and system variability, creating feasibility issues for practical deployment
MIMO computational schemes are also unable to adapt the required computational power, and thereby incur operating power penalties when operating in low-correlation environments, making them less desirable in power-constrained environments.
Hence, present methods do not couple correlation prediction, computational complexity, and MIMO algorithm sophistication. A system and method with these characteristics provides flexibility in spatial multiplexing systems.
Although spatial multiplexing is important for meeting goals of high rate (e.g., 100 Gb/s) RF links for range and capacity, its practical use on long-endurance aerial or fixed, power constrained platforms presents challenges. Motion of spatially separated multi-aperture transmit and receive platforms induces a sampling of the full correlation range of signals which, at high rates, results in prohibitive processing complexity and power consumption for the known MIMO signal processing algorithms. For example, applying a conventional MIMO algorithm such as the list sphere detector (LSD) to a 4×4 polarization multiplexed system (8 transmit streams) in which independent processing over polarizations is possible, would incur a computational cost in excess of 25,000 operations per bit for the MIMO demapper. At the rough computational density per watt (CDW) of a graphics processing unit (GPU) of ˜3 GOPS/W (giga-operations per second per watt), this algorithm would consume over 800 kW of power for 100 Gb/s of throughput. Thus, methods that enable orders-of-magnitude improvements are necessary to meet ambitious high rate transmission goals.